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This paper introduces TriGlue, a novel generative model designed for the computational design of molecular glue degraders by addressing the complexities of ternary complex formation. The framework employs a two-stage approach that first estimates protein-protein interfaces using an SE(3)-equivariant module and then generates molecular glues through an interface-conditioned flow matching network. Experimental results show that TriGlue not only produces chemically valid molecules but also successfully predicts plausible ternary complexes, underscoring its potential to enhance molecular glue discovery.
TriGlue reveals a groundbreaking approach to molecular glue design that effectively combines interface estimation and ternary complex generation, paving the way for targeted protein degradation.
Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein. Despite their therapeutic potential, computational design of molecular glues remains largely unexplored. Unlike conventional structure-based drug design, molecular glue design is governed by the unknown protein-protein interface and requires the simultaneous modeling of ligand generation, protein-protein docking, and ternary complex assembly. In this work, we formulate molecular glue design as a ternary complex generation problem and propose a biology-inspired generative framework, TriGlue. Motivated by the mechanism of molecular glue action, we decompose ternary complex generation into two coupled stages: interface estimation and interface-conditioned complex generation. First, we develop an SE(3)-equivariant interface estimation module that predicts a geometrically constrained protein-protein interface from unbound monomer structures. Second, we introduce an interface-conditioned ternary flow matching network that jointly generates the molecular glue and predicts the rigid-body transformation required to assemble the ternary complex. Extensive experiments demonstrate that TriGlue generates chemically valid molecules and produces plausible ternary complexes, which highlight the potential of biology-inspired generative modeling for accelerating molecular glue discovery. Our code is available at https://github.com/yuliangyan0807/molecular-glue-design.
